Top 7 OpenClaw Agent Templates for 2026

Building AI agents from scratch still takes most teams weeks of prompt engineering, tool integration, and testing. OpenClaw's pre-built agent templates change that equation. Each template ships with tested prompts, model routing, tool connections, and deployment configs that you can have running in hours, then customise over time.

We have deployed all seven of the templates below for Canadian businesses ranging from five-person startups to 500-employee mid-market firms. This post covers what each template does, which models it uses under the hood, how long deployment actually takes, and who gets the most value from it.

If you are new to OpenClaw, start with our overview of the OpenClaw platform before diving into templates.

1. Customer Support Agent

The Customer Support Agent template connects to your knowledge base (help docs, FAQs, product manuals) and handles tier-one support queries over chat, email, or a web widget. It uses retrieval-augmented generation to ground every answer in your actual documentation rather than hallucinating generic responses.

What sets this template apart from a basic chatbot is the auto-escalation logic. When the agent detects that a customer is frustrated, asking about billing disputes, or raising an issue outside its confidence threshold, it routes the conversation to a human agent with full context attached. No dead-end "I can't help with that" messages.

• Knowledge sync: Pulls from Notion, Confluence, Google Docs, or a static file folder. Re-indexes every six hours by default.
• Escalation rules: Configurable by sentiment score, keyword triggers, or number of clarification loops.
• Handoff: Integrates with Intercom, Zendesk, or Freshdesk for seamless agent transfer.

One Toronto-based SaaS company we worked with reduced their support ticket volume by 38% in the first month after deploying this template, with a customer satisfaction score that stayed within two points of their human-only baseline.

2. Sales Outreach Agent

The Sales Outreach Agent automates the research-and-personalise cycle that eats up most of an SDR's day. Given a list of target companies or LinkedIn profiles, the agent researches each prospect, identifies relevant pain points based on their industry and recent activity, and drafts personalised outreach emails.

The dual-model approach matters here. GPT-4o handles the research phase because it excels at synthesising information from multiple sources. Claude handles the email drafting because it produces more natural, less templated prose that avoids the "obviously AI-written" tone that tanks reply rates.

• Lead enrichment: Pulls company size, recent news, tech stack, and hiring signals from public sources.
• Personalisation depth: Goes beyond "I saw your company does X" to reference specific challenges in their vertical.
• Sequence support: Generates multi-touch sequences (initial email, follow-up, breakup email) with consistent messaging.
• CRM sync: Pushes drafts and activity logs to HubSpot or Salesforce.

3. Document Analyzer

Long documents are where most AI tools fall apart. The Document Analyzer template solves this by using Kimi's extended context window to ingest entire contracts, regulatory filings, or research papers (up to 200,000 tokens), then routes specific questions to ChatGPT for concise, structured answers.

The template includes pre-built analysis modes:

• Contract review: Extracts key terms, obligations, renewal dates, and liability clauses into a structured table.
• Regulatory comparison: Compares a document against a regulation checklist and flags gaps or non-compliant language.
• Research synthesis: Summarises multiple papers on the same topic, highlighting areas of agreement and contradiction.
• Due diligence: Scans financial documents for risk indicators, unusual terms, or missing disclosures.

Output can be formatted as a summary memo, a comparison table, or a structured JSON payload for downstream processing.

4. Code Review Agent

The Code Review Agent plugs into your GitHub or GitLab workflow and reviews pull requests automatically. It is not a linter. It reads the diff in the context of the surrounding codebase and provides the kind of feedback a senior engineer would give: architectural concerns, edge cases, naming inconsistencies, and potential performance issues.

The two-model pipeline keeps costs reasonable. MiniMax runs a fast initial scan to categorise the PR (bug fix, feature, refactor) and flag obvious issues. Only PRs that pass the initial filter get the full Claude review, which is more thorough but more expensive per token.

• Inline comments: Posts review comments directly on the PR, linked to specific lines.
• Style enforcement: Checks against your team's coding standards document (uploaded as a reference file).
• Security scan: Flags common vulnerability patterns (SQL injection, XSS, hardcoded secrets).
• Summary: Generates a one-paragraph review summary for the PR author and reviewer.

Teams using this template report that human reviewers spend 40-50% less time on routine PRs, freeing them to focus on complex architectural decisions.

5. Meeting Summarizer

The Meeting Summarizer ingests audio or video recordings from Zoom, Google Meet, or Teams and produces structured meeting notes within minutes of the call ending. It goes beyond a raw transcript to deliver what people actually need: decisions made, action items with owners, open questions, and a brief narrative summary.

• Speaker identification: Tags contributions by participant name (requires a participant list or learns from context).
• Action extraction: Identifies commitments ("I will send the proposal by Friday") and assigns them to the speaker with due dates.
• Topic segmentation: Breaks a one-hour meeting into labelled sections so readers can jump to the part that matters to them.
• Distribution: Automatically sends the summary to a Slack channel, email list, or Notion database.

The template includes a feedback loop: recipients can flag inaccuracies, and the agent adjusts its summarisation patterns for future meetings with the same group.

6. Compliance Monitor

The Compliance Monitor scans your internal documents, policies, and procedures against regulation checklists and flags gaps, outdated language, or missing controls. It is particularly useful for Canadian organisations navigating overlapping federal and provincial requirements.

The template ships with pre-built regulation profiles for:

• PIPEDA: Personal information handling, consent requirements, breach notification obligations.
• OSFI guidelines: Technology and cyber risk management for financial institutions.
• PHIPA: Ontario health information privacy requirements.
• SOC 2 controls: Security, availability, and confidentiality trust service criteria.

You can also create custom regulation profiles by uploading the regulatory text. The agent parses it into a checklist of requirements, then scores your documents against each item. Output is a compliance gap report with severity ratings and suggested remediation steps.

For teams already thinking about security posture, our OpenClaw security hardening guide covers how to lock down agent deployments for sensitive environments.

7. Data Pipeline Agent

The Data Pipeline Agent automates extract-transform-load (ETL) workflows with AI-powered data cleaning and schema mapping. Instead of writing transformation scripts from scratch, you describe the source format, the target schema, and the business rules in plain language. The agent generates and executes the pipeline.

• Schema inference: Reads a sample of source data and proposes column mappings to the target schema.
• Data cleaning: Normalises dates, addresses, phone numbers, and currency formats. Handles Canadian-specific formats (postal codes, province abbreviations, GST/HST fields).
• Anomaly detection: Flags rows that deviate from expected patterns before they land in your data warehouse.
• Incremental runs: Supports scheduled runs with change detection so you are not reprocessing your entire dataset every time.
• Connectors: Pre-built integrations for PostgreSQL, BigQuery, Snowflake, S3, and flat file (CSV/Excel) sources.

A Vancouver-based logistics company used this template to consolidate shipment data from four different carrier APIs into a single reporting schema. What previously took a data analyst two days of manual wrangling per week now runs automatically every morning.

How to Customise Templates for Your Business

Every template works out of the box with sensible defaults, but the real value comes from tailoring the agent to your specific context. Here is the customisation workflow we recommend:

1. Deploy the default template and run it against a sample of your real data or workflows. This gives you a baseline to measure improvements against.
2. Adjust the system prompts. Each template exposes its core prompts in the OpenClaw dashboard. Add your company's terminology, tone of voice, and domain-specific instructions.
3. Connect your data sources. Swap out the sample knowledge base for your actual documentation, CRM, or database. The template handles the connection plumbing; you just provide credentials and schema details.
4. Configure routing rules. Decide which queries the agent handles autonomously, which require human approval, and which get escalated immediately.
5. Set up monitoring. OpenClaw's built-in analytics track response accuracy, latency, cost per query, and user satisfaction. Use these metrics to iterate on prompts and routing.

For more ideas on what to automate, see our OpenClaw automation use cases for Canadian businesses.

Self-Hosted vs Cloud Deployment

OpenClaw supports both deployment models, and the right choice depends on your data sensitivity and ops capacity.

Cloud (OpenClaw Managed)

• One-click deployment from the template library.
• Automatic scaling, updates, and monitoring.
• Data processed in US or Canadian data centres (configurable).
• Best for teams without dedicated DevOps resources or when data sensitivity is moderate.

Self-Hosted (Your Infrastructure)

• Deploy via Docker or Kubernetes on AWS, GCP, Azure, or on-premises.
• Full control over data residency, network rules, and access policies.
• You manage updates, scaling, and infrastructure costs.
• Required for organisations handling protected health information, financial data under OSFI guidelines, or government contracts.

Most of the Canadian enterprises we work with start on the managed cloud for prototyping, then migrate to self-hosted once the agent moves into production with sensitive data. OpenClaw's export function makes this transition straightforward: you export the agent config as a YAML bundle and deploy it to your own cluster.

Frequently Asked Questions